Atrial fibrillation (“AF”) is a very common rhythm disturbance of the heart which affects a significant proportion of the general population and is associated with increased risk of stroke and death. Currently, atrial fibrillation is diagnosed by symptoms or is discovered incidentally. Available evidence indicates that a significant portion of patients with atrial fibrillation do not have symptoms, and consequently the atrial fibrillation of such patients may not be discovered during routine medical examinations. However, atrial fibrillation may be diagnosed using medical equipment, such as rhythm monitors. Monitoring techniques used by available rhythm monitors include monitoring the heart rhythm for a short period of time or monitoring intermittently. Unfortunately, these monitoring techniques have low sensitivity for the detection of atrial fibrillation. Additionally, these rhythm monitors generally have limited storage capacity for storing monitoring data used to determine the extent of atrial fibrillation.
Atrial fibrillation is the most common disturbance of the heart rhythm requiring treatment. Epidemiologic data estimates that 2.2 million individuals suffer from atrial fibrillation in the United States. The incidence of AF increases with age. The prevalence of atrial fibrillation is approximately 2-3% in patients older than 40 years of age and 6% in those individuals over 65 years and 9% in individuals over 80 years old. Feinberg W M, Blackshear J L, Laupacis A, et al. Prevalence, Age Distribution, and Gender of Patients With Atrial Fibrillation, 155 Arch Intern Med. 469 (1995). As the US population ages, atrial fibrillation will become more prevalent. It is estimated that over 5 million Americans will suffer from atrial fibrillation by the year 2050. Go A S, Hylek E M, Phillips K A et al., Prevalence of Diagnosed Atrial Fibrillation in Adults: National Implications for Rhythm Management and Stroke Prevention: the Anticoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study, 285 JAMA, 2370 (2001). Atrial fibrillation is associated with a doubling of mortality rate of people afflicted with atrial fibrillation compared to people who are not, and an increased risk of stroke of about 5% per year. Fuster V, Ryden L E, Asinger R W, et al., ACC/AHA/ESC Guidelines for the Management of Patients With Atrial Fibrillation, 22 Eur. Heart J. 1852 (October 2001).
Atrial fibrillation can be either symptomatic or asymptomatic, and can be paroxysmal or persistent. Symptomatic atrial fibrillation is a medical condition wherein symptoms associated with atrial fibrillation are readily detectable by experts in the field. Atrial fibrillation is usually diagnosed when a patient reveals symptoms or complications associated with atrial fibrillation, such as congestive heart failure or stroke. Atrial fibrillation may also be diagnosed incidentally during a routine medical evaluation. Asymptomatic atrial fibrillation is a medical condition wherein symptoms normally associated with atrial fibrillation are either absent or are not readily detectable by experts in the field. Paroxysmal atrial fibrillation comprises occasional attacks of the atrial fibrillation condition on the patient. Persistent atrial fibrillation is a continuous existence of the atrial fibrillation condition. Patients with asymptomatic paroxysmal atrial fibrillation may be exposed to the risk of devastating consequences of atrial fibrillation such as stroke, congestive heart failure, or tachycardia mediated cardiomyopathy, for years before a definitive diagnosis of atrial fibrillation can be made. Current standard techniques and devices for detecting atrial fibrillation include a resting electrocardiogram, which records about 15 seconds of cardiac activity, a Holter monitor, which records 24-48 hours of cardiac activity during routine daily activities, and an event monitor, which only records cardiac activity when the patient activates the monitor because the patient has detected symptoms associated with atrial fibrillation. These diagnostic methods and tools have significant limitations in diagnosing atrial fibrillation and assessing the efficacy of treatment of atrial fibrillation because of the limited recording time windows of these methods and tools.
The prevalence of asymptomatic atrial fibrillation is difficult to assess, but is clearly underrepresented in the figures quoted above. Pharmacologic treatment of atrial fibrillation may convert patients with symptomatic atrial fibrillation into patients with asymptomatic atrial fibrillation. In a retrospective study of four studies comparing Azimilide drug to placebo where, in the absence of symptoms, routine trans-telephonic electrocardiograms were recorded for 30 seconds every two weeks, asymptomatic atrial fibrillation was present in 17% of the patients. Page R L, Tilsch B S, Connolly S J, et al., Asymptomatic or “Silent Atrial Fibrillation: Frequency in Untreated Patients and Patients Receiving Azimilide, 107 Circulation 1141 (2003). In another study of 110 patients with permanently implanted pacemakers who had a history of atrial fibrillation, atrial fibrillation was diagnosed in 46% of the patients using electrocardiogram (“EKG”) recording and in 88% of the patients using stored electrograms recorded by the implanted pacemaker. Israel C W, Grönfefeld G, Ehrlich J R, et al., Long-Term Risk of Recurrent Atrial Fibrillation as Documented by an Implantable Monitoring Device, 43 J Am Coll Cardiol 47 (2004). Review of data stored in implanted devices, such as pacemakers, revealed that 38% of atrial fibrillation recurrences lasting greater than 48 hours were completely asymptomatic. Finally, using data obtained from ambulatory monitors used on patients with paroxysmal atrial fibrillation over a 24-hour period, studies show a high frequency of occurrence of asymptomatic atrial fibrillation among patients treated with propranolol or propafenone drugs. Wolk R, Kulakowki P, Karczmarewicz S, et al., The Incidence of Asymptomatic Paroxysmal Atrial Fibrillation in Patients Treated With Propranolol or Propafenone, 54 Int J Cardiol 207-(1996). In the above-mentioned study, 22% of the patients on propranolol and 27% of the patients on propafenone were diagnosed with atrial fibrillation without symptoms. There is also evidence that previously undetected atrial fibrillation is associated with stroke. About 4% of patients with stroke admitted to a medical facility also had newly diagnosed atrial fibrillation which was thought to be a precipitating cause of the stroke. Lin H J, Wolf P A, Benjamin E J, Belanger A J, D'Agostino R B, Newly Diagnosed Atrial Fibrillation and Acute Stroke, 26 The Framingham Study 1527 (1995).
Under-detection and under-recognition of atrial fibrillation in patients may have significant clinical consequences. A first consequence includes clinical exposure of patients to a significant risk of cardioembolic stroke before detection of the arrhythmia and initiation of appropriate stroke prevention measures. A second consequence includes difficulty of assessment of the efficacy of rhythm control intervention. Physicians caring for such patients may erroneously conclude that atrial fibrillation is no longer present and inappropriately discontinue anticoagulation treatments which may lead to a devastating cardioembolic stroke. Consequently, once diagnosed with atrial fibrillation, many patients may be committed to life-long anticoagulation by the physician to avoid the latter issues. A third consequence includes overestimation of successful maintenance of sinus rhythm. Clinical studies evaluating the efficacy of various rhythm control strategies may overestimate the successful maintenance of sinus rhythm as many of these studies report symptomatic atrial fibrillation as an endpoint. An accurate long term monitoring device would enhance the diagnostic yield of capturing asymptomatic paroxysmal atrial fibrillation, potentially allowing the safe withdrawal of anticoagulation treatments in patients treated successfully with antiarrhythmic agents, identifying the patients at risk who are currently not diagnosed as having atrial fibrillation, and providing a more precise measure of the efficacy of pharmacologic and nonpharmacologic rhythm control strategies.
Detection of atrial fibrillation, automatically or manually, based on statistical data, requires the use of thresholds defined with respect to sensitivity and specificity. The thresholds used define the point beyond which a set of data indicate existence of atrial fibrillation. Sensitivity and specificity are defined as follows. In a dichotomous experiment, a given event, e, falls into one of two sets, such as a set of positive events, P, and a set of negative events, N. The set P includes events p and the set N includes events n. A detection test may be performed to determine that the given event e belongs to the set P or to the set N in a dichotomous experiment. Sensitivity is a measure of how well the detection test can correctly identify the given event e of the set P as belonging to the set P. Such events e1 that are correctly identified as belonging to the set P are known as true positives (“TP”). Such events e that are misidentified as belonging to the set N are known as false negatives (“FN”). Sensitivity is defined as the ratio of the number of true positive events detected correctly by the test to the total number of actual positive events p. The total number of actual positive events is equal to the sum of the TP and FN. That is, sensitivity=TP/(TP+FN). A low sensitivity detection test will misidentify more positive events as belonging to the set N than a high sensitivity detection test. Specificity is the dual of sensitivity and is a measure of how well the detection test can correctly identify the given event e of the set N as belonging to the set N. Such events e that are correctly identified as belonging to the set N are known as true negatives (“TN”). Such events e that are misidentified as belonging to the set P are known as false positives (“FP”). Specificity is defined as the ratio of the number of true negative events detected correctly by the test to the total number of actual negative events n. The total number of actual negative events is equal to the sum of the TN and FP. That is, specificity=TN/(TN+FP). A low specificity detection test will misidentify more negative events as belonging to the set P than a high specificity detection test.
A number of techniques have been used for the automated detection of atrial fibrillation from digitized electrocardiograms. One of the techniques used includes the use of intracardiac recordings obtained from implanted devices showing a sensitivity of close to 100% and a specificity of greater than 99%. Swerdlow C D, Schsls W, Dijkman B, Jung W, Sheth N V, Olson W H, Gunderson B D, Detection of A trial Fibrillation and Flutter by a Dual-Chamber Implantable Cardioverter-Defibrillator, 101 Circulation 878 (2000). A method for analysis of the surface monitor leads using a wavelet transform achieved a sensitivity of 96% and specificity of 93% in recordings from patients with paroxismal atrial fibrillation. Duverney D, Gaspoz J M, Pichot V, Roche F, Brion R, Antoniadis A, Barthelemy J C, High Accuracy of Automatic Detection of Atrial Fibrillation Using Wavelet Transform of Heart Rate Intervals, 25 Pacing Clin Electrophysiol 457 (2002). At least one group has proposed using wavelets for implantable/wearable monitoring devices. Ang N H., Real-Time Electrocardiogram (ECG) Signal Processing for Atrial Fibrillation (AF) Detection, Modeling Seminar—Archive (2003). A prominent characteristic of atrial fibrillation is heart rate variability. There have been attempts to use the variability of heart interbeat (“RR”) intervals directly to identify atrial fibrillation, resulting in a sensitivity of 94% and specificity of 97% using a threshold based on the Kolmogorov-Smirnov test. Tateno K, Glass L, Automatic Detection of Atrial Fibrillation Using the Coefficient of Variation and Density Histograms of Rr and Deltarr Intervals, 39 Med Biol Eng Comput. 664 (2001). The Kolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used to decide if a statistical sample belongs to a population with a specific probability distribution.
Long-term monitoring of cardiac activity is desirable for timely detection of atrial fibrillation, but the storage requirements can be prohibitive. To digitize a single channel EKG at 100 samples per second and 10-bit resolution, which constitute near minimum requirements for a high quality signal, for 90 days of continuous recording requires 927 mega bytes (million bytes, “MB”) of storage. Although providing this amount of storage is possible, it is also costly. Advances in electronics allow the design of portable devices that can pre-process and classify the signals to avoid storage of normal rhythms and save the storage capacity for recording of abnormal rhythms indicating existence of atrial fibrillation. Selective storage of signals that potentially indicate atrial fibrillation as opposed to normal heart rhythm, effectively increases the storage capacity and prolongs the recording period. At least two such devices exist in the market. One such device has been developed by Instromedix (San Diego, Calif.), and is available in two versions. Each version can monitor the heart rhythm for up to 30 days, capturing a total of 10 minutes of potentially abnormal EKG. The device weighs about 4 ounces. The other device is based on satellite telephone technology, and transmits the suspect rhythms to a monitoring facility. Recently, another device was announced with a detection rate of 90% and a monitoring storage capacity equivalent to 60 minutes of recorded data. A device for home use which does “momentary” analysis of the electrocardiogram as the patient grasps handles on the device daily is disclosed in U.S. Pat. No. 6,701,183, issued to Lohman Mar. 2, 2004, entitled Long Term Atrial Fibrillation Monitor.
A device is desired for the long-term monitoring of atrial fibrillation that is inexpensive, non-invasive, highly accurate, and convenient for the patient. These requirements at least indicate that the monitoring device should be light and small. As such, a device is desired with low power requirements and with a significant amount of storage. The storage capacity may possibly be extended by using an algorithm for the elimination of EKG data that indicate very low-probability of atrial fibrillation. This algorithm should be small in size and simple in operation to reduce processing power needs and electrical power requirements. The existing algorithms based on wavelets appear to be overly complex for this type of application requiring a significant amount of processing and electrical power as well as storage capacity.